Panoramic images can be created by an array of wide angle cameras that together create up to a 360 degree field of view or by one camera with a fish eye lens or other panoramic mirror that allows for a continuous “mirror ball” image that is later flattened out by computer.
A relatively new means of capturing panoramic images is by continuously spinning a thermal sensor or other high speed camera at less than 60 RPM and processing the images from the camera with a computer where they may be stitched together and analyzed.
A common first step for performing video analysis is to develop a background model from successive video frames and then to compare new frames against that background model to look for changes that could be foreground movement. As some background objects (such as trees, banners, etc.) can have movement and change, a certain amount of tolerance should be built in for movement to the analysis to view these objects as background and not foreground objects. This tolerance is typically set for the entire video image and used for all changes regardless of where they are in the video frame.
Relatedly, object classification in computer vision requires identifying characteristics about a foreground object that make it a likely match to a real world object, such as a person, animal or vehicle. Calculations performed to identify these characteristics can be computationally expensive, limiting the amount of analysis that can be performed on embedded or lower power systems.
The subject matter claimed herein is not limited to embodiments that solve any disadvantages or that operate only in environments such as those described above. Rather, this background is only provided to illustrate one exemplary technology area where some embodiments described herein may be practiced.